A Hybrid DQN–PID Control Framework With Multisensor Fusion for Enhanced Docking Performance of Autonomous Mobile Robots in Complex Environments

This study proposes a hybrid control framework that integrates a Deep Q-Network (DQN), adaptive PID control, and multi-sensor fusion via an Extended Kalman Filter (EKF) to enhance the accuracy, stability, and adaptability of autonomous mobile robots (AMRs) during docking tasks in complex indoor environments. A neural network dynamically tunes PID parameters based on the robot’s state, combining the robustness of classical control with the flexibility of learning-based methods. For localization, AprilTag visual markers are fused with multi-sensor data through EKF, yielding more accurate state estimation. A taskspecific reward function incorporates target distance, angular deviation, collision penalties, and docking incentives, guiding the learning process toward smooth and efficient trajectories. Cosine-based angular velocity modulation and a LiDAR-triggered mode selector enable seamless switching between DQN-PID control and a modified DQN policy with smoother motion and faster reward convergence. While conventional DQN suffers from unsmooth motion and slower reward convergence, experimental results in both simulated and real-world environments show that the proposed switching framework achieves nearly 100% docking success, greatly surpassing the DQN-only approach, which gained only 59%. These results demonstrate clear advantages in convergence speed, trajectory smoothness, and robustness, confirming the framework’s suitability for real-world autonomous docking applications.